Background/Objectives: Computed tomography (CT)-based radiomic analysis is an emerging technique that enables non-invasive assessment of tumor characteristics. In gastrointestinal stromal tumors (GISTs), radiomics may reflect biological behavior such as proliferative activity, often indicated by Ki-67 expression. To our knowledge, this is the first systematic review and meta-analysis synthesizing evidence on the ability of CT radiomics to predict the Ki-67 index in GISTs, addressing an important gap in the literature.Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines to evaluate the predictive performance of CT radiomics for Ki-67 expression in GISTs. A literature search of PubMed, Scopus, Science Direct, and the Cochrane Library was performed up to December 2024 using predefined terms. Extracted data included study design, patient demographics, imaging protocols, radiomic features, and diagnostic performance. Study quality was assessed using the QUADAS-2 tool. A random-effects meta-analysis summarized the pooled area under the ROC curve (AUC), sensitivity, and specificity. Subgroup and sensitivity analyses explored heterogeneity sources. Publication bias was assessed using Egger’s test and funnel plots.Results: Six studies involving 1632 patients were included. The pooled sensitivity and specificity for predicting Ki-67 expression were 0.71 and 0.76, respectively, with a summary AUC of 0.79. Subgroup analyses showed consistent results across different imaging protocols and radiomic feature sets, though the Ki-67 cutoff (8% vs. 10%) affected diagnostic performance. Moderate heterogeneity and potential publication bias in specificity were observed.Conclusions: CT-based radiomics demonstrates moderate accuracy for non-invasively predicting Ki-67 index in GISTs. While not a substitute for histology, it may support personalized preoperative planning and guide future immunotherapy strategies. In the future, radiomic signatures—particularly when integrated with molecular or immune-related biomarkers—could help refine patient selection and monitoring strategies for emerging therapies, including immunotherapy.
背景/目的：基于计算机断层扫描（CT）的影像组学分析是一种新兴技术，能够无创评估肿瘤特征。在胃肠道间质瘤（GIST）中，影像组学可能反映肿瘤的生物学行为，如通常由Ki-67表达所指示的增殖活性。据我们所知，这是首次系统综述和荟萃分析，综合评估CT影像组学预测GIST中Ki-67指数的能力，填补了文献中的重要空白。 方法：遵循PRISMA指南，我们进行了一项系统综述和荟萃分析，以评估CT影像组学预测GIST中Ki-67表达的效能。截至2024年12月，使用预定义术语在PubMed、Scopus、Science Direct和Cochrane图书馆进行了文献检索。提取的数据包括研究设计、患者人口统计学特征、成像方案、影像组学特征和诊断性能。研究质量使用QUADAS-2工具进行评估。采用随机效应模型进行荟萃分析，汇总了受试者工作特征曲线下面积（AUC）、敏感性和特异性。通过亚组分析和敏感性分析探讨异质性来源。使用Egger检验和漏斗图评估发表偏倚。 结果：共纳入6项研究，涉及1632名患者。预测Ki-67表达的汇总敏感性和特异性分别为0.71和0.76，汇总AUC为0.79。亚组分析显示，尽管Ki-67截断值（8% vs. 10%）影响了诊断性能，但在不同的成像方案和影像组学特征集中结果一致。观察到中度异质性以及特异性方面存在潜在的发表偏倚。 结论：基于CT的影像组学在无创预测GIST Ki-67指数方面显示出中等准确性。虽然不能替代组织学检查，但它可能有助于个性化的术前规划，并指导未来的免疫治疗策略。未来，影像组学特征——特别是与分子或免疫相关生物标志物结合时——可能有助于优化患者选择，并为包括免疫治疗在内的新兴疗法制定监测策略。
肿瘤(癌症)患者之家